Intelligent Machines Lab Reading Group

The Intelligent Machines Lab Reading Group meets every Friday at 10:30 A.M. in LT-1 at Information Technology University, Lahore. We present and discuss publications, tools and experiments from variety of areas like Computer Vision, Deep Learning, Machine Learning, Robotics, Social Robots, Human Robot Interactions and Geographic Information System (GIS). These meetings are very informal, everyone (faculty members, students, research fellows etc.) is welcomed to join us. Our objective is to provide venue for sharing new developments, enhance mathematical rigor and have fun helping each other.

Organizer: Afsheen Rafaqat Ali

Please email at if you would like to present a paper at the group.

Upcoming Presentations

Date Presenter Paper or topic
August 25, 2017 Mohsin Riaz Design of an Energy-Efficient Accelerator for Training of Convolutional Neural Networks using Frequency-Domain Computation [pdf]
Authors: Jong Hwan Ko, Burhan Mudassar, Taesik Na, and Saibal Mukhopadhyay. Design Automation Conference, – 2017

Previous Presentations

Date Presenter Paper or topic
August 18, 2017 Afsheen Rafaqat Ali Emotion Recognition in Context [pdf]
Authors: Ronak Kosti, Jose M. Alvarez, Adria Recasens, Agata Lapedriza, CVPR – 2017
August 11, 2017 Dr. Mudassir Shabbir Introduction to VC Dimesions
August 3, 2017 Anza Shakeel Multi-Scale Dense Convolutional Networks for Efficient Prediction [pdf]
Authors: Gao Huang(Cornell University), Danlu Chen(Fudan University), Tianhong Li (Tsinghua University), Felix Wu (Cornell University), Arxiv Paper 2017
July 28, 2017 Mohsen Ali Densely Connected Convolutional Networks [pdf]
Authors: Gao Huang, Zhuang Liu, Kilian Q. Weinberger, Laurens van der Maaten
Published in Conference on Computer Vision and Pattern Recognition, 2017 and received best paper award.
June 22, 2017 Afsheen Rafaqat Ali Is Faster R-CNN Doing Well for Pedestrian Detection? [pdf]
Authors: Liliang Zhang, Liang Lin, Xiaodan Liang, Kaiming He
Published in European Conference on Computer Vision, 2016.
June 9, 2017 Muhammad Nawaz Program sketching [pdf]
Authors: Armando Solar-Lezama
Published in International Journal on Software Tools for Technology Transfer, 2013.
June 2, 2017 Dr. Mohsen Ali Learning Deep Features for Discriminative Localization [pdf]
Authors: Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba
Published in Conference on Computer Vision and Pattern Recognition, 2016.
May 26, 2017 Muhammad Athar
Towards Robot Autonomy in Group Conversations: Understanding the Effects of Body Orientation and Gaze [pdf]
Authors: Marynel Vazquez, Elizabeth J. Carter, Braden McDorman, Jodi Forlizzi, Aaron Steinfeld, Scott Hudson
Published in March, 2017 in ACM/IEEE International Conference on Human-Robot Interaction, Vienna, Austria.
May 19, 2017 Muhammad Usama Human-level control through deep reinforcement learning [pdf]
Authors: Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis HassabisPublished on 25 February 2015 in Nature
May 12, 2017 Afsheen Rafaqat Ali Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [pdf]
Authors: Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros
Submitted to ICCV 2017
May 5, 2017 Tehreem Aslam Scribbler: Controlling Deep Image Synthesis with Sketch and Color [pdf]
Authors: Patsorn Sangkloy, Jingwan Lu, Chen Fang, Fisher Yu, James Hays
Computer Vision and Pattern Recognition, CVPR 2017
April 28, 2017 Muhammad Faisal and
Sanaullah Manzoor
LipNet: End-To-End Sentence Level Lipreading [pdf]
Authors: Yannis M. Assael, Brendan Shillingford, Shimon Whiteson, Nando de Freitas
Arxiv Paper
April 21, 2017 Anza Shakeel Unsupervised Visual Representation Learning By Context Prediction [pdf]
Authors: Carl Doersch, Abhinav Gupta, Alexei A. Efros
Oral paper at ICCV 2015
April 21, 2017 Umair Shoaib UberNet: Training a ‘Universal’ Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory [pdf]
Author: Iasonas Kokkinos
Arxiv Paper